Abstract

Recognizing Melakartha raagas from speech has gained immense attention recently. With the increasing demand for human computer interaction, it is necessary to understand the state of the singer. In this paper an attempt is made to recognize and classify the raagas from the singers database where the classification is mainly based on extracting several key features like Mel Frequency Cepstral Coefficients (MFCCs) from the speech signals of those persons by using the process of feature extraction. For training and testing of the method, data is collected from the existing database (both genders) with due verification relating to melakartha raagas of singers from Andhra Pradesh, India. Out of 72 melakartha raagas (36 male 36 female) available for training, a few raagas (both genders) were specifically selected and tested. Then it is found that all the tested raagas are well recognized. In another case the 50 melakartha raagas for training and another 22 raagas for testing. The experiments were performed pertaining to singer dependent and independent raagas. Using a statistical model like Gaussian Mixture Model classifier (GMM) and features extracted from these speech signals, an unique identity was built for each raaga that enrolled for raaga recognition. Expectation and Maximization (EM) algorithm, an elegant and powerful method, is used with latent variables for finding the maximum likelihood solution, to test the other raagas against the database of all singers who enrolled in the database.

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